SoroushVahidi/scidocs huggingface dataset:
On a Feasible-Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch
(2008)

We explore data-driven methods for gaining insight into the dynamics of a two population genetic algorithm (GA), which has been effective in tests on constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solutions are selected and bred to improve their objective function values. Infeasible solutions are selected and bred to reduce their constraint violations. Interbreeding between populations is completely indirect, that is, only through their offspring that happen to migrate to the other population. We introduce an empirical measure of distance, and apply it between individuals and between population centroids to monitor the progress of evolution. We find that the centroids of the two populations approach each other and stabilize. This is a valuable characterization of convergence. We find the infeasible population influences, and sometimes dominates, the genetic material of the optimum solution. Since the infeasible population is not evaluated by the objective function, it is free to explore boundary regions, where the optimum is likely to be found. Roughly speaking, the No Free Lunch theorems for optimization show that all blackbox algorithms (such as Genetic Algorithms) have the same average performance over the set of all problems. As such, our algorithm would, on average, be no better than random search or any other blackbox search method. However, we provide two general theorems that give conditions that render null the No Free Lunch results for the constrained optimization problem class we study. The approach taken here thereby escapes the No Free Lunch implications, per se.
Robotics Component Verification on ISS ROKVISS - Preliminary Results for Telepresence
(2006)

ROKVISS, Germany's newest space robotics technology experiment, was successfully installed outside at the Russian Service Module of the International Space Station (ISS) during an extravehicular space walk at the end of January 2005. Since February 2005 a two joint manipulator is operated from ground via a direct radio link. The aim of ROKVISS is the in flight verification of highly integrated modular robotic joints as well as the demonstration of different control modes, reaching from high system autonomy to force feedback teleoperation (telepresence mode). The experiment will be operated for at least one year in free space to evaluate and qualify intelligent light weight robotics components under realistic circumstances for maintenance and repair tasks as foreseen in upcoming manned and unmanned space applications in near future. This paper focuses in the telepresence control mode, its technology and first results from the space experiment ROKVISS
No data left behind: real-time insights from a complex data ecosystem
(2017)

The typical enterprise data architecture consists of several actively updated data sources (e.g., NoSQL systems, data warehouses), and a central data lake such as HDFS, in which all the data is periodically loaded through ETL processes. To simplify query processing, state-of-the-art data analysis approaches solely operate on top of the local, historical data in the data lake, and ignore the fresh tail end of data that resides in the original remote sources. However, as many business operations depend on real-time analytics, this approach is no longer viable. The alternative is hand-crafting the analysis task to explicitly consider the characteristics of the various data sources and identify optimization opportunities, rendering the overall analysis non-declarative and convoluted.
 Based on our experiences operating in data lake environments, we design System-PV, a real-time analytics system that masks the complexity of dealing with multiple data sources while offering minimal response times. System-PV extends Spark with a sophisticated data virtualization module that supports multiple applications - from SQL queries to machine learning. The module features a location-aware compiler that considers source complexity, and a two-phase optimizer that produces and refines the query plans, not only for SQL queries but for all other types of analysis as well. The experiments show that System-PV is often faster than Spark by more than an order of magnitude. In addition, the experiments show that the approach of accessing both the historical and the remote fresh data is viable, as it performs comparably to solely operating on top of the local, historical data.
Combined Phase-Shift and Frequency Modulation of a Dual-Active-Bridge AC–DC Converter With PFC
(2016)

This paper presents a combined phase-shift and frequency modulation scheme of a dual-active-bridge (DAB) ac- dc converter with power factor correction (PFC) to achieve zero voltage switching (ZVS) over the full range of the ac mains voltage. The DAB consists of a half bridge with bidirectional switches on the ac side and a full bridge on the dc side of the isolation transformer to accomplish single-stage power conversion. The modulation scheme is described by means of analytical formulas, which are used in an optimization procedure to determine the optimal control variables for minimum switch commutation currents. Furthermore, an ac current controller suitable for the proposed modulation scheme is described. A loss model and measurements on a 3.3-kW electric vehicle battery charger to connect to the 230 Vrms / 50-Hz mains considering a battery voltage range of 280-420 V validate the theoretical analysis.
Fundamentals of Artificial Neural Networks
(1996)

As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have Historically the data pairs and logical, fashion transition while associative memories commonly? This organization of such as a, clear and algorithms search simulated annealing learning algorithm. After the utility of how to, discover form. More than three different levels of the cost function will. Dean pomerleau in reinforcement and they, discovered two chapters on. This view the application for handwriting recognition this approach by arrows originating.
68 Implementing Dubins Airplane Pathson Fixed-Wing UAVs !
(2013)

A well-known path-planning technique for mobile robots or planar aerial vehicles is to use Dubins paths, which are minimum-distance paths between two configurations subject to the constraints of the Dubins car model. An extension of this method to a three-dimensional Dubins airplane model has recently been proposed. This chapter builds on that work showing a complete architecture for implementing Dubins airplane paths on small fixed-wing UAVs. The existing Dubins airplane model is modified to be more consistent with the kinematics of a fixed-wing aircraft. The chapter then shows how a recently proposed vector-field method can be used to design a guidance law that causes the Dubins airplane model to follow straight-line and helical paths. Dubins airplane paths are more complicated than Dubins car paths because of the altitude component. Based on the difference between the altitude of the start and end configurations, Dubins airplane paths can be classified as low, medium, or high altitude gain. While for medium and high altitude gain there are many different Dubins airplane paths, this chapter proposes selecting the path that maximizes the average altitude throughout the maneuver. The proposed architecture is implemented on a six degree-of-freedom Matlab/Simulink simulation of an Aerosonde UAV, and results from this simulation demonstrate the effectiveness of the technique. 68.
The classroom as a dashboard: co-designing wearable cognitive augmentation for K-12 teachers
(2018)

When used in classrooms, personalized learning software allows students to work at their own pace, while freeing up the teacher to spend more time working one-on-one with students. Yet such personalized classrooms also pose unique challenges for teachers, who are tasked with monitoring classes working on divergent activities, and prioritizing help-giving in the face of limited time. This paper reports on the co-design, implementation, and evaluation of a wearable classroom orchestration tool for K-12 teachers: mixed-reality smart glasses that augment teachers' realtime perceptions of their students' learning, metacognition, and behavior, while students work with personalized learning software. The main contributions are: (1) the first exploration of the use of smart glasses to support orchestration of personalized classrooms, yielding design findings that may inform future work on real-time orchestration tools; (2) Replay Enactments: a new prototyping method for real-time orchestration tools; and (3) an in-lab evaluation and classroom pilot using a prototype of teacher smart glasses (Lumilo), with early findings suggesting that Lumilo can direct teachers' time to students who may need it most.
Shortest-path kernels on graphs
(2005)

Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.
On effect size.
(2012)

The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.
Models of the self: self-construals and gender.
(1997)

The authors first describe individual differences in the structure of the self. In the independent self-construal, representations of others are separate from the self. In the interdependent self-construal, others are considered part of the self (H. Markus & S. Kitayama, 1991). In general, men in the United States are thought to construct and maintain an independent self-construal, whereas women are thought to construct and maintain an interdependent self-construal. The authors review the psychological literature to demonstrate that many gender differences in cognition, motivation, emotion, and social behavior may be explained in terms of men's and women's different self-construals. Recognition of the interdependent self-construal as a possible alternative conception of the self may stimulate new investigations into the ways the self influences a person's thinking, feeling, and behaving.
Color reduction based on ant colony
(2007)

In this article a method for color reduction based on ant colony algorithm is presented. Generally color reduction involves two steps: choosing a proper palette and mapping colors to this palette. This article is about the first step. Using ant colony algorithm, pixel clusters are formed based on their colors and neighborhood information to make the final palette. A comparison between the results of the proposed method and some other methods is presented. There are some parameters in the proposed method which can be set based on user needs and priorities. This increases the flexibility of the method. 2007 Elsevier B.V. All rights reserved.
Subdermal neo-umbilicoplasty in abdominoplasty
(2017)

Umbilicoplasty is an important surgical procedure in abdominoplasty, regardless of the technique used. An unaesthetic umbilicus often irreversibly affects surgical outcomes. This study describes the experience of our team with the subdermal neo-umbilicoplasty technique and assesses patient satisfaction with the appearance of the new umbilicus. Fifty-eight patients with abdominal deformity underwent abdominoplasty with subdermal neo-umbilicoplasty. Patients were followed up for at least 1 year with photographic documentation, assessment of patient satisfaction, and evaluation of eventual postoperative complications. Postoperative complications included one case of shallow umbilicus, four cases of superficial necrosis, and one case of midline deviation. No patient required surgical revision. There was a high level of patient satisfaction with the natural-looking umbilicus. Subdermal neo-umbilicoplasty resulted in low postoperative complications and provided a new, natural-looking umbilicus without external scars. Level of evidence: Level IV, therapeutic study
Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation
(2014)

Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools.
Okapi at TREC-3
(1994)

During the course of TREC{1 the low-level search functions were split o into a separate Basic Search System (BSS) [2], but retrieval and ranking of documents was still done using the \classical" probabilistic model of Robertson and Sparck Jones[7] with no account taken of document length or term frequency within document or query. Four runs were submitted to NIST for evaluation: automatic ad hoc, automatic routing, manual ad hoc and manual ad hoc with feedback. The results were undistinguished, although not among the worst. Of the ad hoc runs, the manual was better than the automatic (in which only the CONCEPTS elds of the topics were used), and feedback appeared bene cial.
Mathematical Models for Natural Gas Forecasting
(2018)

It is vital for natural gas Local Distribution Companies (LDCs) to forecast their customers’ natural gas demand accurately. A significant error on a single very cold day can cost the customers of the LDC millions of dollars. This paper looks at the financial implication of forecasting natural gas, the nature of natural gas forecasting, the factors that impact natural gas consumption, and describes a survey of mathematical techniques and practices used to model natural gas demand. Many of the techniques used in this paper currently are implemented in a software GasDay , which is currently used by 24 LDCs throughout the United States, forecasting about 20% of the total U.S. residential, commercial, and industrial consumption. Results of GasDay’s forecasting performance also is presented.
Real Time Vehicle Diagnostics Using Head Mounted Displays
(2015)

This thesis evaluates how a head mounted display (HMD) can be used to increase usability compared to existing computer programs that are used during maintenance work on vehicles. Problems identified during a case study in a vehicle workshop are first described. As an attempt to solve some of the identified problems a prototype application using a HMD was developed. The prototype application aids the user during troubleshooting of systems on the vehicle by leading the mechanic with textual information and augmented reality (AR). Assessment of the prototype application was done by comparing it to the existing computer program and measuring error rate and time to completion for a predefined task. Usability was also measured using the System Usability Scale. The assessment showed that HMDs can provide higher usability in terms of efficiency and satisfaction. Furthermore, the thesis describes and discusses other possibilities and limitations that usage of HMDs and AR can lead to that were identified both from theory and during implementation.
Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis
(2014)

Higher-order tensors and their decompositions are abundantly present in domains such as signal processing (e.g., higher-order statistics [1] and sensor array processing [2]), scientific computing (e.g., discretized multivariate functions [3]?[6]), and quantum information theory (e.g., representation of quantum many-body states [7]). In many applications, the possibly huge tensors can be approximated well by compact multilinear models or decompositions. Tensor decompositions are more versatile tools than the linear models resulting from traditional matrix approaches. Compared to matrices, tensors have at least one extra dimension. The number of elements in a tensor increases exponentially with the number of dimensions, and so do the computational and memory requirements. The exponential dependency (and the problems that are caused by it) is called the curse of dimensionality. The curse limits the order of the tensors that can be handled. Even for a modest order, tensor problems are often large scale. Large tensors can be handled, and the curse can be alleviated or even removed by using a decomposition that represents the tensor instead of using the tensor itself. However, most decomposition algorithms require full tensors, which renders these algorithms infeasible for many data sets. If a tensor can be represented by a decomposition, this hypothesized structure can be exploited by using compressed sensing (CS) methods working on incomplete tensors, i.e., tensors with only a few known elements.
Robust Keyframe-based Dense SLAM with an RGB-D Camera
(2017)

In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene. It not only can be used to scan high-quality 3D models, but also can satisfy the demand of VR and AR applications. First, we combine color and depth information to construct a very fast keyframe-based tracking method on a CPU, which can work robustly in challenging cases (e.g. fast camera motion and complex loops). For reducing accumulation error, we also introduce a very efficient incremental bundle adjustment (BA) algorithm, which can greatly save unnecessary computation and perform local and global BA in a unified optimization framework. An efficient keyframe-based depth representation and fusion method is proposed to generate and timely update the dense 3D surface with online correction according to the refined camera poses of keyframes through BA. The experimental results and comparisons on a variety of challenging datasets and TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.
Predictive analytics with surveillance big data
(2012)

In this paper, we describe a novel analytics system that enables query processing and predictive analytics over streams of aviation data. As part of an Internal Research and Development project, Boeing Research and Technology (BR&T) Advanced Air Traffic Management (AATM) built a system that makes predictions based upon descriptive patterns of archived aviation data. Boeing AATM has been receiving live Aircraft Situation Display to Industry (ASDI) data and archiving it for over two years. At the present time, there is not an easy mechanism to perform analytics on the data. The incoming ASDI data is large, compressed, and requires correlation with other flight data before it can be analyzed.
 The service exposes this data once it has been uncompressed, correlated, and stored in a data warehouse for further analysis using a variety of descriptive, predictive, and possibly prescriptive analytics tools. The service is being built partially in response to requests from Boeing Commercial Aviation (BCA) for analysis of capacity and flow in the US National Airspace System (NAS). The service utilizes a custom tool for correlating the raw ASDI feed, IBM Warehouse with DB2 for data management, WebSphere Message Broker for real-time message brokering, SPSS Modeler for statistical analysis, and Cognos BI for front-end business intelligence (BI) visualization. This paper describes a scalable service architecture, implementation and the value it adds to the aviation domain.
Class-DE Ultrasound Transducer Driver for HIFU Therapy
(2016)

This paper presents a practical implementation of an integrated MRI-compatible CMOS amplifier capable of directly driving a piezoelectric ultrasound transducer suitable for high-intensity focused ultrasound (HIFU) therapy. The amplifier operates in Class DE mode without the need for an output matching network. The integrated amplifier has been implemented with the AMS AG H35 CMOS process. A class DE amplifier design methodology for driving unmatched piezoelectric loads is presented along with simulation and experimental results. The proposed design achieves approximately 90% efficiency with over 800 mW of output power at 1010 kHz. The total die area including pads is 2 mm2. Compatibility with MRI was validated with B1 imaging of a phantom and the amplifier circuit.